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Summary: RLVQ determination using OWA operators
Angel Cat¸aron
Department of Electronics and Computers
Transylvania University of Bras¸ov, Romania
email: cataron@vega.unitbv.ro
Razvan Andonie
Computer Science Department
Central Washington University, Ellensburg, USA
email: andonie@deltanet.ro
ABSTRACT
Relevance Learning Vector Quantization (RLVQ) (intro-
duced in [1]) is a variation of Learning Vector Quantiza-
tion (LVQ) which allows a heuristic determination of rel-
evance factors for the input dimensions. The method is
based on Hebbian learning and defines weighting factors
of the input dimensions which are automatically adapted to
the specific problem. These relevance factors increase the
overall performance of the LVQ algorithm. At the same
time, relevances can be used for feature ranking and input
dimensionality reduction.
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